Systems and methods for utilizing a 3D CAD point-cloud to automatically create a fluid model

ABSTRACT

A multiple fluid model tool for utilizing a 3D CAD point-cloud to automatically create a fluid model is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional design component. The modeling component generates a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system. The machine learning component predicts one or more characteristics of the mechanical device based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional design component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device based on the input data and the one or more characteristics of the mechanical device on the three-dimensional model.

CROSS-REFERENCE

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/517,154, filed Jul. 19, 2019, and entitled“SYSTEMS AND METHODS FOR UTILIZING A 3D CAD POINT-CLOUD TO AUTOMATICALLYCREATE A FLUID MODEL”, which is a continuation of and claims priority toU.S. patent application Ser. No. 15/630,939, filed Jun. 22, 2017, (U.S.Pat. No. 10,409,950) and entitled “SYSTEMS AND METHODS FOR UTILIZING A3D CAD POINT-CLOUD TO AUTOMATICALLY CREATE A FLUID MODEL”, which claimspriority to U.S. Provisional Patent Application No. 62/469,953, filedMar. 10, 2017, and entitled “A MULTIPLE FLUID MODEL TOOL FORINTERDISCIPLINARY FLUID MODELING.” The entireties of the foregoingapplications listed herein are hereby incorporated by reference.

TECHNICAL FIELD

This disclosure relates generally to three dimensional modeling systems,and more specifically, to modeling of a fluid system and/or a fluidsystem design tool.

BACKGROUND

During a design phase of a device or product associated with a fluidsystem, it is often desirable to determine impact of a fluid withrespect to the device or product associated with the fluid system. Todetermine impact of the fluid with respect to the design, numericalanalysis of two dimensional (2D) data associated with computationalfluid dynamics can be employed to analyze fluid flow through the deviceor product. For instance, a color of a 2D surface associated with thedevice or product can represent a degree of fluid flow. However,analyzing impact of a fluid with respect to a design of the device orproduct generally involves human interpretation of 2D data, which canresult in human trial and error with respect to the fluid system.Moreover, human interpretation of 2D data and/or employing multiplefluid model tools to determine impact of a fluid with respect to adesign of a device or product can be burdensome with respect to cost,redundancy and/or maintenance associated with the device or product.

SUMMARY

The following presents a simplified summary of the specification inorder to provide a basic understanding of some aspects of thespecification. This summary is not an extensive overview of thespecification. It is intended to neither identify key or criticalelements of the specification, nor delineate any scope of the particularimplementations of the specification or any scope of the claims. Itssole purpose is to present some concepts of the specification in asimplified form as a prelude to the more detailed description that ispresented later.

In accordance with an embodiment, a system includes a modelingcomponent, a machine learning component, and a three-dimensional designcomponent. The modeling component generates a three-dimensional model ofa mechanical device based on point cloud data indicative of informationfor a set of data values associated with a three-dimensional coordinatesystem. The machine learning component predicts one or morecharacteristics of the mechanical device based on input data and amachine learning process associated with the three-dimensional model.The three-dimensional design component provides a three-dimensionaldesign environment associated with the three-dimensional model. Thethree-dimensional design environment renders physics modeling data ofthe mechanical device based on the input data and the one or morecharacteristics of the mechanical device on the three-dimensional model.

In accordance with another embodiment, a method provides for generating,by a system comprising a processor, a three-dimensional model of amechanical device based on point cloud data indicative of informationfor a set of data values associated with a three-dimensional coordinatesystem. The method also provides for predicting, by the system, fluidflow and physics behavior associated with the three-dimensional modelbased on input data and a machine learning process associated with thethree-dimensional model. Furthermore, the method provides for rendering,by the system, physics modeling data of the mechanical device based onthe fluid flow and the physics behavior.

In accordance with yet another embodiment, a computer readable storagedevice comprising instructions that, in response to execution, cause asystem comprising a processor to perform operations, comprising:generating a three-dimensional model of a mechanical device based onpoint cloud data indicative of information for a set of data valuesassociated with a three-dimensional coordinate system, performing amachine learning process associated with the three-dimensional model topredict one or more characteristics of the mechanical device, andproviding a three-dimensional design environment associated with thethree-dimensional model that renders physics modeling data of themechanical device based on the machine learning process.

The following description and the annexed drawings set forth certainillustrative aspects of the specification. These aspects are indicative,however, of but a few of the various ways in which the principles of thespecification may be employed. Other advantages and novel features ofthe specification will become apparent from the following detaileddescription of the specification when considered in conjunction with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee. Numerous aspects, implementations, objects andadvantages of the present invention will be apparent upon considerationof the following detailed description, taken in conjunction with theaccompanying drawings, in which like reference characters refer to likeparts throughout, and in which:

FIG. 1 illustrates a high-level block diagram of an example fluid modeltool component, in accordance with various aspects and implementationsdescribed herein;

FIG. 2 illustrates a high-level block diagram of an example fluid modeltool component associated with flow prediction, in accordance withvarious aspects and implementations described herein;

FIG. 3 illustrates a high-level block diagram of an example fluid modeltool component associated with thermal prediction, in accordance withvarious aspects and implementations described herein;

FIG. 4 illustrates a high-level block diagram of an example fluid modeltool component associated with combustion prediction, in accordance withvarious aspects and implementations described herein;

FIG. 5 illustrates a high-level block diagram of an example fluid modeltool component in communication with a user display device, inaccordance with various aspects and implementations described herein;

FIG. 6 illustrates an example system that facilitates overlaying and/orintegrating computer aided design drawings with fluid models, inaccordance with various aspects and implementations described herein;

FIG. 7 illustrates an example system that facilitates overlaying and/orintegrating computer aided design drawings with fluid models, inaccordance with various aspects and implementations described herein;

FIG. 8 illustrates an example 3D model, in accordance with variousaspects and implementations described herein;

FIG. 9 illustrates an example 3D model, in accordance with variousaspects and implementations described herein;

FIG. 10 illustrates another example 3D model, in accordance with variousaspects and implementations described herein;

FIG. 11 illustrates yet another example 3D model, in accordance withvarious aspects and implementations described herein;

FIG. 12 illustrates yet another example 3D model, in accordance withvarious aspects and implementations described herein;

FIG. 13 depicts a flow diagram of an example method for providinginterdisciplinary fluid modeling using point cloud data, in accordancewith various aspects and implementations described herein;

FIG. 14 depicts a flow diagram of another example method for providinginterdisciplinary fluid modeling using point cloud data, in accordancewith various aspects and implementations described herein;

FIG. 15 depicts a flow diagram of yet another example method forproviding interdisciplinary fluid modeling using point cloud data, inaccordance with various aspects and implementations described herein;

FIG. 16 depicts a flow diagram of yet another example method forproviding interdisciplinary fluid modeling using point cloud data, inaccordance with various aspects and implementations described herein;

FIG. 17 is a schematic block diagram illustrating a suitable operatingenvironment; and

FIG. 18 is a schematic block diagram of a sample-computing environment.

DETAILED DESCRIPTION

Various aspects of this disclosure are now described with reference tothe drawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of one or more aspects. It should beunderstood, however, that certain aspects of this disclosure may bepracticed without these specific details, or with other methods,components, materials, etc. In other instances, well-known structuresand devices are shown in block diagram form to facilitate describing oneor more aspects.

Systems and techniques that provide a multiple fluid model tool forutilizing a three-dimensional (3D) computer aided design (CAD)point-cloud to automatically create a fluid model are presented. Forexample, as compared to conventional analysis of a fluid system thatinvolves human interpretation of two-dimensional (2D) data and/or humantrial and error with respect to a fluid system, the subject innovationsprovide for a 3D design environment that can be generated by utilizing a3D CAD point-cloud to automatically create a fluid model. In an aspect,physics modeling data associated with a degree of fluid flow can berendered on a 3D model of a device. In one example, visualcharacteristics of the physics modeling data can be dynamic based on thedegree of fluid flow. Various systems and techniques disclosed hereincan be related to cloud-based services, a heating, ventilation and airconditioning (HVAC) system, a medical system, an automobile, anaircraft, a water craft, a water filtration system, a cooling system,pumps, engines, diagnostics, prognostics, optimized machine designfactoring in cost of materials in real-time, explicit and/or implicittraining of models through real-time aggregation of data, etc. In anembodiment, a multiple fluid model tool can provide a platform forinterdisciplinary fluid modeling by utilizing a 3D CAD point-cloud toautomatically create a fluid model. As such, a fluid model can comprisepoint-cloud aspects. Bi-directional integration with CAD data for adevice associated with the fluid model can also be realized to provideup-to-date data for the fluid model. In an aspect, 3D CAD point-cloudscan be employed to automatically create computational domains and/orcontrol volumes (e.g., chambers/elements/components) for the fluidmodel. The fluid model can also be employed by a flow integrated heattransfer and combustion design environment solver to generatepredictions for simulated machine conditions for the device. As such, a3D model associated with physics modeling can be generated moreefficiently and/or data provided by a 3D model associated with physicsmodeling can be more accurate. Moreover, damage to a device, machineand/or component associated with a 3D model can be minimized byreplacing human trial and error for analyzing one or morecharacteristics associated with the 3D model of the device, machineand/or component.

Referring initially to FIG. 1, there is illustrated an example system100 that provides a multiple fluid model tool for interdisciplinaryfluid modeling, according to an aspect of the subject disclosure. Thesystem 100 can be employed by various systems, such as, but not limitedto modeling systems, aviation systems, power systems, distributed powersystems, energy management systems, thermal management systems,transportation systems, oil and gas systems, mechanical systems, machinesystems, device systems, cloud-based systems, heating systems, HVACsystems, medical systems, automobile systems, aircraft systems, watercraft systems, water filtration systems, cooling systems, pump systems,engine systems, diagnostics systems, prognostics systems, machine designsystems, medical device systems, medical imaging systems, medicalmodeling systems, simulation systems, enterprise systems, enterpriseimaging solution systems, advanced diagnostic tool systems, imagemanagement platform systems, artificial intelligence systems, machinelearning systems, neural network systems, and the like. In one example,the system 100 can be associated with a graphical user interface systemto facilitate visualization and/or interpretation of 3D data. Moreover,the system 100 and/or the components of the system 100 can be employedto use hardware and/or software to solve problems that are highlytechnical in nature (e.g., related to processing 3D data, related tomodeling 3D data, related to artificial intelligence, etc.), that arenot abstract and that cannot be performed as a set of mental acts by ahuman.

The system 100 can include a fluid model tool component 102 that caninclude a modeling component 104, a machine learning component 106and/or a 3D design component 108. The modeling component 104 can includea point cloud component 105. In an aspect, modeling performed by thefluid model tool component 102 can be associated with a flow integrateddesign environment, a heat transfer design environment and/or acombustion design environment. Aspects of the systems, apparatuses orprocesses explained in this disclosure can constitute machine-executablecomponent(s) embodied within machine(s), e.g., embodied in one or morecomputer readable mediums (or media) associated with one or moremachines. Such component(s), when executed by the one or more machines,e.g., computer(s), computing device(s), virtual machine(s), etc. cancause the machine(s) to perform the operations described. The system 100(e.g., the fluid model tool component 102) can include memory 110 forstoring computer executable components and instructions. The system 100(e.g., the fluid model tool component 102) can further include aprocessor 112 to facilitate operation of the instructions (e.g.,computer executable components and instructions) by the system 100(e.g., the fluid model tool component 102). In certain embodiments, thesystem 100 can further include a library of data elements 114. Thelibrary of data elements 114 can be a library of stored data elements.

The modeling component 104 can generate a 3D model of a device. The 3Dmodel can be a 3D representation of the device for presentation via a 3Ddesign environment. In an embodiment, the modeling component 104 cangenerate a 3D model of a mechanical device and/or an electronic device.In an aspect, the modeling component 104 can generate a 3D model of adevice based on point cloud data indicative of information for a set ofdata values associated with a 3D coordinate system. The point cloud datacan define a shape, a geometry, a size, one or more surfaces and/orother physical characteristics of the device. For instance, the pointcloud data can include 3D data points (e.g., 3D vertices) that form ashape, a structure and/or a set of surfaces of the device via a 3Dcoordinate system. As such, the point cloud data can be employed tocreate a 3D representation of the device using the set of data valuesassociated with the 3D coordinate system. The 3D coordinate system canbe, for example, a 3D Cartesian coordinate system that includes anx-axis, a y-axis and a z-axis. As such, a data value included in thepoint cloud data can be associated with an x-axis value, a y-axis valueand a z-axis value to identify a location of the data value with respectto the 3D coordinate system. In an embodiment, the modeling component104 can generate a 3D model of a device based on, for example, thelibrary of data elements 114. The library of data elements 114 caninclude a set of data elements for mechanical components and/orelectrical components. Furthermore, the set of data elements caninclude, for example, the point cloud data and/or texture data. As such,library of data elements 114 can be formed based on the point clouddata. In an non-limiting example, the library of data elements 114 caninclude a data element for fluid source, a fuel source, flow elements,pipe systems, sealing systems, pressure drop components (e.g., orifices,valves, fittings, junctions, transitions, etc.), diffusers, heatexchangers, controllers, pumps, fans, compressors, cavities, vortexesand/or other components. Additionally or alternatively, the library ofdata elements 114 can include experimental data (e.g., experimental testdata) associated with the device. For example, the library of dataelements 114 can include one or more properties of the device that isdetermined via one or more experiments and/or one or more researchprocesses. The one or more experiments and/or one or more researchprocesses can include determining and/or capturing the one or moreproperties via a physical representation of the device associated withthe 3D model. The one or more properties of the device can include, forexample, one or more physical properties of the device, one or moremechanical properties of the device, one or more measurements of thedevice, one or more material properties of the device, one or moreelectrical properties of the device, one or more thermal properties ofthe device and/or one or more other properties of the device.

In certain embodiments, the modeling component 104 can perform modelingof one or more mechanical elements of a device (e.g., a mechanicaldevice and/or an electronic device). For example, the modeling component104 can determine a set of boundaries for features of mechanicalelements of the device. Furthermore, the modeling component 104 candetermine a set of physical characteristics for mechanical elements. Ina non-limiting example, the modeling component 104 can determine one ormore chambers of a device based on the point cloud data. The modelingcomponent 104 can, for example, determine a set of boundaries thatdefine the one or more chambers based on the point cloud data. Themodeling component 104 can also determine, based on the point clouddata, a set of physical characteristics for the one or more chamberssuch as, for example, a size for the one or more chambers, a shape forthe one or more chambers, a volume of the one or more chambers and/oranother physical characteristic for the one or more chambers. In anaspect, the modeling component 104 can compute the one or moremechanical elements of the device based on the point cloud data. Tocompute the one or more mechanical elements, the modeling component 104can employ one or more modeling techniques using the point cloud data.As such, the one or more mechanical elements can be one or morecomputationally derived elements. In another aspect, the modelingcomponent 104 can perform a modeling process associated with the one ormore modeling techniques to facilitate design of a system associatedwith the device, where the system includes a set of mechanical elementsthat are combined to form the device.

In an embodiment, the modeling component 104 can determine a set ofcontrol volumes associated with the device based on the point clouddata. For instance, the modeling component 104 can overlay a set ofcontrol volumes on the device using the point cloud data. A controlvolume can be an abstraction of a region of the device through which afluid (e.g., a liquid or a gas) and/or an electrical current flows. Inone example, a control volume can correspond to a chamber of the device.The modeling component 104 can determine geometric features of the setof control volumes using the point cloud data. For instance, themodeling component 104 can determine computational control volumes(e.g., chambers) and/or geometrical features of the computationalcontrol volumes using the point cloud data. Control volumes can beconnected via various types of preconfigured elements and/orpreconfigured components to construct an analysis computational modelthat extends from supply to sink conditions. Control volumes can alsosimulate run conditions for the preconfigured elements, thepreconfigured components and/or a system associated with the 3D model.The preconfigured elements and/or the preconfigured components can begenerated based on the point cloud data and/or stored in the library ofdata elements 114, for example. For instance, the library of dataelements 114 can include an extended library of preconfigured elementsand/or preconfigured components generated from the point cloud data. Theextended library of preconfigured elements and/or preconfiguredcomponents can be employed by the modeling component 104 to facilitatemodeling and/or simulating a wide-range of physical phenomena includingcompressible/incompressible fluid flow, buoyancy driven flow, rotatingcavity system flow, conduction/convection/radiation heat transfer,combustion equilibrium-chemistry, species transport, etc. Physicalformulation of the preconfigured elements and/or the preconfiguredcomponents can be varied based on complexity of a physical phenomena tobe simulated. In an aspect, physical formulation of the preconfiguredelements and/or the preconfigured components can categorized asmachine-learning based elements (e.g., seals, leakages, compressors,fans, junctions, bends, valves, orifices, pipes, etc.). Additionally oralternatively, physical formulation of the preconfigured elements and/orthe preconfigured components can be categorized as computationallyderived based elements (e.g., modeling methods utilizing a combinationof analytical modeling techniques and experimental test data).Combination of the preconfigured elements and/or the preconfiguredcomponents can be employed by the modeling component 104 to constructthe 3D model that can be further employed (e.g., by the machine learningcomponent 106) to simulate and/or predict a machine steady state ortransient response.

The modeling component 104 can employ the point cloud data toautomatically create computational domains and/or control volumes (e.g.,chambers/elements/components) for the 3D model that can be employed(e.g., by the machine learning component 106) to generate predictionsfor simulated machine conditions for a device associated with the 3Dmodel. Automation of the computational model creation can significantlyreduce the cycle time of analysis setup. In an aspect, the modelingcomponent 104 can determine a computational geometry for the deviceassociated with 3D model based on the point cloud data. For example, themodeling component 104 can determine a computational geometry for acontrol volume associated with the 3D model based on the point clouddata.

In an embodiment, the point cloud component 105 of the modelingcomponent 104 can be employed to manage the point cloud data. Forexample, the point cloud component 105 can generate identification dataindicative of an identifier for the control volume associated with the3D model that is generated based on the point cloud data. Theidentification data can include, for example, geometric data indicativeof geometric features of the control volume, surface data indicative ofsurface information for the control volume, curvature data indicative ofa curvature of the control volume and/or other data associated with thecontrol volume. In one example, the identification data can include atag that tags and/or links the control volume to 3D CAD data associatedwith the device and/or the 3D model. The tag can include, for example,one or more boundary conditions for the control volume, a type ofprocessing for the control volume (e.g., loading information to generatea virtualized geometry for the control volume), and/or other informationfor the control volume. In an aspect, the tag can include, for example,point-cloud geometric tags that identifies a geometry of the controlvolume, CAD curves parametric expressions that provide a set ofparameters for the control volume, surfaces parametric tags thatdescribe surface information for the control volume, etc. The CAD curvesparametric expressions can be, for example, a set of algebraic equationsfor the control volume. In one example, the CAD curves parametricexpressions can define the control volume based on one or more othergeometric entities within the 3D model. In another embodiment, the pointcloud component 105 of the modeling component 104 can update the controlvolume based on the identification data. For instance, in response tomodification of CAD data associated with the 3D model and/or the device,the point cloud component 105 of the modeling component 104 canidentify, using the identification data, one or more portions of thecontrol volume associated with the modification of the CAD data. Theidentification data can provide a bi-directional link between thecontrol volume and the CAD data. As such, a control volume and/or othercomputational domains can be automatically updated when the CAD data isupdated. In yet another embodiment, the point cloud component 105 cangenerate one or more missing portions of CAD data associated with the 3Dmodel. For example, the point cloud component 105 can generatevirtualized geometry, one or more virtualized surfaces and/or otherinformation missing from the CAD data. The point cloud component 105 canalso perform further processing on the CAD data using point cloud data.In an aspect, the point cloud component 105 can compute links for one ormore data points and/or lines included in the CAD data, curvature forone or more portions in the CAD data and/or a surface area for one ormore portions in the CAD data. In yet another embodiment, the modelingcomponent 104 can integrate sub-components of a device (e.g., amechanical device and/or an electronic device) and/or sub-models of adevice (e.g., a mechanical device and/or an electronic device) to form,for example, sub-combinations and/or models of an entire machine. In anaspect, the modeling component 104 can integrate a first flow network ofa first sub-component with a second flow network of a secondsub-component. Additionally or alternatively, the modeling component 104can integrate first heat transfer throughout a first sub-component withsecond heat transfer throughout a second sub-component. Additionally oralternatively, the modeling component 104 can integrate first multiphaseflow through a first sub-component with second multiphase flow through asecond sub-component.

The machine learning component 106 can perform learning (e.g., explicitlearning and/or implicit learning) and/or can generate inferences withrespect to one or more 3D models generated by the modeling component104. The learning and/or generated inferences by the machine learningcomponent 106 can facilitate determination of one or morecharacteristics associated with the one or more 3D models generated bythe modeling component 104. The learning and/or generated inferences canbe determined by the machine learning component 106 via one or moremachine learning processes associated with the one or more 3D models.The one or more characteristics determined by the machine learningcomponent 106 can include, for example, one or more fluidcharacteristics associated with the one or more 3D models generated bythe modeling component 104, one or more thermal characteristicsassociated with the one or more 3D models generated by the modelingcomponent 104, one or more combustion characteristics associated withthe one or more 3D models generated by the modeling component 104, oneor more electrical characteristics associated with the one or more 3Dmodels generated by the modeling component 104 and/or one or more othercharacteristics associated with the one or more 3D models generated bythe modeling component 104. In an aspect, the machine learning component106 can predict and/or model a flow network of a mechanical elementassociated with the one or more 3D models, heat transfer throughout amechanical element associated with the one or more 3D models, combustionassociated with a mechanical element associated with the one or more 3Dmodels, multiphase flow through a mechanical element associated with theone or more 3D models and/or other characteristics of a mechanicalelement associated with the one or more 3D models.

In an embodiment, the machine learning component 106 can predict the oneor more characteristics associated with the one or more 3D models basedon input data and one or more machine learning processes associated withthe one or more 3D models. The input data can be, for example, a set ofparameters for a fluid capable of flowing through the one or more 3Dmodels, a set of parameters for a thermal energy capable of flowingthrough the one or more 3D models, a set of parameters for a combustionchemical reaction capable of flowing through the one or more 3D models,a set of parameters for electricity flowing through the one or more 3Dmodels, and/or another set of parameters for input provided to the oneor more 3D models. The one or more characteristics associated with theone or more 3D models can correspond to one or more characteristics ofthe device (e.g., the mechanical device and/or the electronic device).In one example, distinct types of control volumes (e.g., chambers)simulating reservoirs, volume mixing dynamics, volume inertial dynamics,volume pumping dynamics, and/or volume gravitational dynamics can beemployed by the machine learning component 106 to model and/or simulatevarious fluid flow conditions associated with the one or more 3D models.In an aspect, the machine learning component 106 can also employmeasured data and/or streamed data to set boundary conditions for one ormore machine learning processes. For example, the machine learningcomponent 106 can also employ measured data and/or streamed data to setboundary conditions for supply chambers and sink chambers and/or toestablish driving forces for simulated physics phenomena (e.g., fluiddynamics, thermal dynamics, combustion dynamics, angular momentum,etc.).

Additionally or alternatively, the machine learning component 106 canperform a probabilistic based utility analysis that weighs costs andbenefits related to the one or more 3D models generated by the modelingcomponent 104. The machine learning component 106 (e.g., one or moremachine learning processes performed by the machine learning component106) can also employ an automatic classification system and/or anautomatic classification process to facilitate learning and/orgenerating inferences with respect to the one or more 3D modelsgenerated by the modeling component 104. For example, the machinelearning component 106 (e.g., one or more machine learning processesperformed by the machine learning component 106) can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to learn and/or generate inferenceswith respect to the one or more 3D models generated by the modelingcomponent 104. The machine learning component 106 (e.g., one or moremachine learning processes performed by the machine learning component106) can employ, for example, a support vector machine (SVM) classifierto learn and/or generate inferences with respect to the one or more 3Dmodels generated by the modeling component 104. Additionally oralternatively, the machine learning component 106 (e.g., one or moremachine learning processes performed by the machine learning component106) can employ other classification techniques associated with Bayesiannetworks, decision trees and/or probabilistic classification models.Classifiers employed by the machine learning component 106 (e.g., one ormore machine learning processes performed by the machine learningcomponent 106) can be explicitly trained (e.g., via a generic trainingdata) as well as implicitly trained (e.g., via receiving extrinsicinformation). For example, with respect to SVM's that are wellunderstood, SVM's are configured via a learning or training phase withina classifier constructor and feature selection module. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, xn), toa confidence that the input belongs to a class—that is, f(x)=confidence(class).

In an aspect, the machine learning component 106 can include aninference component that can further enhance automated aspects of themachine learning component 106 utilizing in part inference based schemesto facilitate learning and/or generating inferences with respect to theone or more 3D models generated by the modeling component 104. Themachine learning component 106 (e.g., one or more machine learningprocesses performed by the machine learning component 106) can employany suitable machine-learning based techniques, statistical-basedtechniques and/or probabilistic-based techniques. For example, themachine learning component 106 (e.g., one or more machine learningprocesses performed by the machine learning component 106) can employexpert systems, fuzzy logic, SVMs, Hidden Markov Models (HMMs), greedysearch algorithms, rule-based systems, Bayesian models (e.g., Bayesiannetworks), neural networks, other non-linear training techniques, datafusion, utility-based analytical systems, systems employing Bayesianmodels, etc. In another aspect, the machine learning component 106(e.g., one or more machine learning processes performed by the machinelearning component 106) can perform a set of machine learningcomputations associated with the one or more 3D models generated by themodeling component 104. For example, the machine learning component 106(e.g., one or more machine learning processes performed by the machinelearning component 106) can perform a set of clustering machine learningcomputations, a set of decision tree machine learning computations, aset of instance-based machine learning computations, a set of regressionmachine learning computations, a set of regularization machine learningcomputations, a set of rule learning machine learning computations, aset of Bayesian machine learning computations, a set of deep Boltzmannmachine computations, a set of deep belief network computations, a setof convolution neural network computations, a set of stackedauto-encoder computations and/or a set of different machine learningcomputations.

In an embodiment, the modeling component 104 can integrate a first 3Dmodel associated with a first device (e.g., a first mechanical deviceand/or a first electronic device) and a second 3D model associated witha second device (e.g., a second mechanical device and/or a secondelectronic device) to generate a 3D model for a device. For example, a3D model generated by the modeling component 104 can be a combination oftwo or more 3D models. In an aspect, first geometric features of thefirst 3D model can be combined with second geometric features of thesecond 3D model based on the point cloud data. The first geometricfeatures of the first 3D model can include, for example, chambers,cavities, channels, and/or other geometric features associated withpoint cloud data for the first 3D model. Similarly, the second geometricfeatures of the second 3D model can include, for example, chambers,cavities, channels, and/or other geometric features associated withpoint cloud data for the second 3D model. As such, chambers, cavities,channels, and/or other geometric features of the first 3D model and thesecond 3D model can be combined based on the point cloud data. Inanother embodiment, the first 3D model can comprise a first set ofsupply nodes and a first set of sink nodes that form a first flownetwork for characteristics of the first 3D model. For instance, fluidprovided through the first 3D model can flow from a supply node to asink node of the first 3D model. Additionally, the second 3D model cancomprise a second set of supply nodes and a second set of sink nodesthat form a second flow network for characteristics of the second 3Dmodel. For instance, fluid provided through the second 3D model can flowfrom a supply node to a sink node of the second 3D model. The modelingcomponent 104 can combine the first flow network of the first 3D modelwith the second flow network of the second 3D model. For example, thefirst set of supply nodes of the first 3D model can be combined with thesecond set of supply nodes of the second 3D model. Furthermore, thefirst set of sink nodes of the first 3D model can be combined with thesecond set of sink nodes of the second 3D model.

In another embodiment, the machine learning component 106 can perform afirst machine learning process associated with the first 3D model and asecond machine learning process associated with the second 3D model. Forinstance, the machine learning component 106 can perform learning (e.g.,explicit learning and/or implicit learning) and/or can generateinferences with respect to the first 3D model via the first machinelearning process. Furthermore, the machine learning component 106 canperform learning (e.g., explicit learning and/or implicit learning)and/or can generate inferences with respect to the second 3D model viathe second machine learning process. The learning and/or generatedinferences by the machine learning component 106 can facilitatedetermination of one or more characteristics associated with the one ormore 3D models generated by the modeling component 104. Furthermore, thelearning and/or generated inferences can be determined by the machinelearning component 106 via one or more machine learning processesassociated with the one or more 3D models. In an aspect, the machinelearning component 106 can predict one or more characteristics of thedevice based on the one or more first characteristics associated withthe first 3D model and the one or more second characteristics associatedwith the second 3D model. In one example, the machine learning component106 can predict the one or more characteristics of the device based onthe one or more first characteristics and the one or more secondcharacteristics. The one or more first characteristics can include firstfluid flow characteristics associated with the first 3D model, firstthermal characteristics associated with the first 3D model, firstcombustion characteristics associated with the first 3D model and/orfirst physics behavior characteristics associated with the first 3Dmodel. Furthermore, one or more second characteristics can includesecond fluid flow characteristics associated with the second 3D model,second thermal characteristics associated with the second 3D model,second combustion characteristics associated with the second 3D modeland/or second physics behavior characteristics associated with thesecond 3D model. In an embodiment, the machine learning component 106can facilitate interaction between the first 3D model and the second 3Dmodel based on the input data associated with the machine learningcomponent 106. For example, interaction of the one or more firstcharacteristics associated with the first 3D model and the one or moresecond characteristics associated with the second 3D model can bedetermined by the machine learning component 106 based on the inputdata.

The 3D design component 108 can provide a 3D design environmentassociated with the 3D model generated based on the point cloud data andthe machine learning. For instance, the 3D design component 108 canprovide a 3D design environment associated with a mechanical elementand/or a 3D model generated by the modeling component 104. The 3D designenvironment can be a single fluid system design tool. For example, the3D design environment can be a tool that provides functionality ofnumerous tools with respect to fluid systems to providemulti-disciplinary type analyses. In one example, the 3D designenvironment can provide a flow integrated design environment, a heattransfer design environment and/or a combustion design environment. Inanother example, the 3D design environment can be a combustion designenvironment solver associated with the 3D design component 108. The 3Ddesign environment associated with the 3D design component 108 can beemployed to apply one or more numerical schemes to create predictionsfor machine simulated conditions. Prediction can be displayed andanalyzed on a visual representation of actual hardware using apost-processing module of a graphical user interface. In an aspect, the3D design environment associated with the 3D design component 108 cangenerate simulation predictions by conserving governing conservationequations for mass, momentum, energy, angular momentum, and/or speciesutilizing numerical analysis schemes. In certain embodiments, the fluidmodel tool component 102 can be employed as a service. For example, the3D model associated with the fluid model tool component 102 can be agenerated computational model employed by the 3D design environment.

In an embodiment, the 3D design environment can render physics modelingdata of the device based on the input data and the one or morecharacteristics of the mechanical device on the 3D model. The physicsmodeling data can be indicative of a visual representation of the fluidflow, the thermal characteristics, the combustion characteristics and/orthe physics behavior with respect to the 3D model. The physics modelingdata can also be rendered on the 3D model as one or more dynamic visualelements. In an aspect, the 3D design component 108 can alter visualcharacteristics (e.g., color, size, hues, shading, etc.) of at least aportion of the physics modeling data based on the fluid flow, thethermal characteristics, the combustion characteristics and/or thephysics behavior. For example, different degrees of fluid flow throughthe 3D model can be presented as different visual characteristics (e.g.,colors, sizes, hues or shades, etc.), different degrees of thermalcharacteristics with respect to the 3D model can be presented asdifferent visual characteristics (e.g., colors, sizes, hues or shades,etc.), different degrees of combustion characteristics with respect tothe 3D model can be presented as different visual characteristics (e.g.,colors, sizes, hues or shades, etc.), different degrees of physicsbehavior with respect to the 3D model can be presented as differentvisual characteristics (e.g., colors, sizes, hues or shades, etc.), etc.In another aspect, the 3D design environment for the 3D model can allowa user to zoom into or out from the 3D model associated with the physicsmodeling data, rotate a view for the 3D model associated with thephysics modeling data, etc. As such, a user can view, analyze and/orinteract with the 3D model associated with the physics modeling data tofacilitate determination of impact of a fluid flow, thermalcharacteristics, combustion characteristics and/or physics behavior withrespect to a design of the device associated with the 3D model.

Referring now to FIG. 2, there is illustrated an example system 200 thatprovides a multiple fluid model tool for interdisciplinary fluidmodeling, according to an aspect of the subject disclosure. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 200 can include the fluid model tool component 102 and/or thelibrary of data elements 114. The fluid model tool component 102 caninclude the modeling component 104, the machine learning component 106,the 3D design component 108, the memory 110 and/or the processor 112.The modeling component 104 can include the point cloud component 105. Inthe embodiment shown in FIG. 2, the machine learning component 106 caninclude a flow prediction component 202. The flow prediction component202 can predict fluid flow and physics behavior associated with the 3Dmodel generated using the point cloud data. For instance, the flowprediction component 202 can perform a machine learning processassociated with fluid flow through the 3D model generated using thepoint cloud data. The flow prediction component 202 can perform themachine learning process based on input data indicative of inputreceived by a device associated with the 3D model generated using thepoint cloud data. For example, the input data can include fluid dataindicative of a fluid provided to a device associated with the 3D modelgenerated using the point cloud data. The fluid data can include one ormore properties of the fluid such as, for example, a fluid type of thefluid, a density of the fluid, a viscosity of the fluid, a volume of thefluid, a weight of the fluid, a temperature of the fluid and/or anotherproperty of the fluid. The input data can by employed by the flowprediction component 202 to predict the fluid flow. The fluid flow canbe, for example, fluid flow of the input data (e.g., the fluid) throughthe device associated with the 3D model generated using the point clouddata. The physics behavior can be physics behavior of the fluid flow.For instance, the physics behavior can be simulated physics and/orchanges of the fluid flow. Furthermore, the physics behavior can besimulated fluid flow conditions associated with the 3D model generatedusing the point cloud data. The physics behavior can also includecorrelations and/or behavior determined based on one or moremathematical equations associated with fluid flow such as, for example,conservation equations for mass associated with a fluid, conservationequations for momentum associated with a fluid, conservation equationsfor energy associated with a fluid, conservation equations for angularmomentum associated with a fluid, and/or another mathematical equationassociated with fluid flow.

Referring now to FIG. 3, there is illustrated an example system 300 thatprovides a multiple fluid model tool for interdisciplinary fluidmodeling, according to an aspect of the subject disclosure. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 300 can include the fluid model tool component 102 and/or thelibrary of data elements 114. The fluid model tool component 102 caninclude the modeling component 104, the machine learning component 106,the 3D design component 108, the memory 110 and/or the processor 112.The modeling component 104 can include the point cloud component 105. Inthe embodiment shown in FIG. 3, the machine learning component 106 caninclude a thermal prediction component 302. In certain embodiments, themachine learning component 106 can include the thermal predictioncomponent 302 and the flow prediction component 202. The thermalprediction component 302 can predict thermal characteristics and physicsbehavior associated with the 3D model generated using the point clouddata. For instance, the thermal prediction component 302 can perform amachine learning process associated with thermal characteristicsassociated with the 3D model generated using the point cloud data. Thethermal prediction component 302 can perform the machine learningprocess based on input data indicative of input received by a deviceassociated with the 3D model generated using the point cloud data. Forexample, the input data can include the fluid data indicative of a fluidprovided to a device associated with the 3D model generated using thepoint cloud data. Additionally or alternatively, the input data caninclude electrical data indicative of a voltage and/or a currentprovided to a device associated with the 3D model generated using thepoint cloud data. The input data can by employed by the thermalprediction component 302 to predict the thermal characteristics. Thethermal characteristics can be, for example, a temperature associatedwith one or more regions of the 3D model generated using the point clouddata, a heat capacity associated with one or more regions of the 3Dmodel generated using the point cloud data, thermal expansion associatedwith one or more regions of the 3D model generated using the point clouddata, thermal conductivity associated with one or more regions of the 3Dmodel generated using the point cloud data, thermal stress associatedwith one or more regions of the 3D model generated using the point clouddata, and/or another thermal characteristics associated with one or moreregions of the 3D model generated using the point cloud data. Thephysics behavior can be physics behavior of the thermal characteristics.For instance, the physics behavior can be simulated physics and/orchanges of the thermal characteristics. Furthermore, the physicsbehavior can be simulated thermal conditions associated with the 3Dmodel generated using the point cloud data. The physics behavior canalso include correlations and/or behavior determined based on one ormore mathematical equations associated with thermal characteristics suchas, for example, conservation equations for mass associated with thermalcharacteristics, conservation equations for momentum associated withthermal characteristics, conservation equations for energy associatedwith thermal characteristics, conservation equations for angularmomentum associated with thermal characteristics, and/or anothermathematical equation associated with thermal characteristics.

Referring now to FIG. 4, there is illustrated an example system 400 thatprovides a multiple fluid model tool for interdisciplinary fluidmodeling, according to an aspect of the subject disclosure. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 400 can include the fluid model tool component 102 and/or thelibrary of data elements 114. The fluid model tool component 102 caninclude the modeling component 104, the machine learning component 106,the 3D design component 108, the memory 110 and/or the processor 112.The modeling component 104 can include the point cloud component 105. Inthe embodiment shown in FIG. 4, the machine learning component 106 caninclude a combustion prediction component 402. In certain embodiments,in addition to the combustion prediction component 402, the machinelearning component 106 can include the flow prediction component 202and/or the thermal prediction component 302. The combustion predictioncomponent 402 can predict combustion characteristics and physicsbehavior associated with the 3D model generated using the point clouddata. For instance, the combustion prediction component 402 can performa machine learning process associated with combustion characteristicsassociated with the 3D model generated using the point cloud data. Thecombustion prediction component 402 can perform the machine learningprocess based on input data indicative of input received by a deviceassociated with the 3D model generated using the point cloud data. Forexample, the input data can include the fluid data indicative of a fluidprovided to a device associated with the 3D model generated using thepoint cloud data. Additionally or alternatively, the input data caninclude electrical data indicative of a voltage and/or a currentprovided to a device associated with the 3D model generated using thepoint cloud data. Additionally or alternatively, the input data caninclude chemical data indicative of a chemical element provided to adevice associated with the 3D model generated using the point clouddata. The input data can by employed by the combustion predictioncomponent 402 to predict the combustion characteristics. The combustioncharacteristics can be, for example, information related to a chemicalreaction associated with one or more regions of the 3D model such as,for example, a temperature measurement, a heating value, an elementalcomposition, a moisture content, a density, an acoustic measurementand/or another combustion characteristic associated with one or moreregions of the 3D model. The physics behavior can be physics behavior ofthe combustion characteristics. For instance, the physics behavior canbe simulated physics and/or changes of the combustion characteristics.Furthermore, the physics behavior can be simulated combustion conditionsassociated with the 3D model generated using the point cloud data. Thephysics behavior can also include correlations and/or behaviordetermined based on one or more mathematical equations associated withcombustion characteristics such as, for example, conservation equationsfor mass associated with combustion characteristics, conservationequations for momentum associated with combustion characteristics,conservation equations for energy associated with combustioncharacteristics, conservation equations for angular momentum associatedwith combustion characteristics, and/or another mathematical equationassociated with combustion characteristics.

Referring now to FIG. 5, there is illustrated an example system 500 thatprovides a multiple fluid model tool for interdisciplinary fluidmodeling, according to an aspect of the subject disclosure. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 500 can include the fluid model tool component 102, thelibrary of data elements 114 and a user display device 502. The userdisplay device 502 can be in communication with the fluid model toolcomponent 102 via a network 504. The network 504 can be a wired networkand/or a wireless network. The fluid model tool component 102 caninclude the modeling component 104, the machine learning component 106,the 3D design component 108, the memory 110 and/or the processor 112.The modeling component 104 can include the point cloud component 105. Incertain embodiments, the machine learning component 106 can include theflow prediction component 202, the thermal prediction component 302and/or the combustion prediction component 402. The user display device502 can display a 3D model and/or a 3D design environment generated bythe fluid model tool component 102. For example, a 3D model associatedwith the physics modeling data can be rendered on a graphical userinterface associated with a display of the user display device 502. Theuser display device 502 can be a device with a display such as, but notlimited to, a computing device, a computer, a desktop computer, a laptopcomputer, a monitor device, a smart device, a smart phone, a mobiledevice, a handheld device, a tablet, a portable computing device oranother type of user device associated with a display.

Referring now to FIG. 6, there is illustrated an example system 600 thatfacilitates overlaying and/or integrating computer aided design drawingswith fluid models, according to an aspect of the subject disclosure.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

The system 600 can include a 3D model 602. The 3D model 602 can begenerated by the modeling component 104. The 3D model 602 can beassociated with a device (e.g., a mechanical device and/or an electricaldevice). In an aspect, the 3D model 602 can be generated based on pointcloud data (e.g., POINT CLOUD DATA shown in FIG. 6). The point clouddata can be digital data indicative of information for a set of datavalues associated with a 3D coordinate system. The point cloud data candefine a shape, a geometry, a size, one or more surfaces and/or otherphysical characteristics of the device. For instance, the point clouddata can include 3D data points (e.g., 3D vertices) that form a shape, astructure and/or a set of surfaces of the device via a 3D coordinatesystem. The 3D coordinate system can be, for example, a 3D Cartesiancoordinate system that includes an x-axis, a y-axis and a z-axis. Assuch, a data value included in the point cloud data can be associatedwith an x-axis value, a y-axis value and a z-axis value to identify alocation of the data value with respect to the 3D coordinate system. Incertain embodiments, the 3D model 602 can additionally or alternativelybe generated based on the library of data elements 114 where one or moreelements of the library of data elements 114 is constructed from thepoint cloud data. The system 600 can also include a machine learningprocess 604. The machine learning process 604 can be performed by themachine learning component 106. Furthermore, the machine learningprocess 604 can be a machine learning process associated with the 3Dmodel 602. In an aspect, the machine learning component 106 can performthe machine learning process 604 based on 3D model data (e.g., 3D MODELDATA shown in FIG. 6). The machine learning component 106 can alsoperform the machine learning process 604 based on learning and/orgenerated inferences associated with the 3D model 602. Additionally, thesystem 600 can include a physics 3D model 606. The physics 3D model 606can be associated with the 3D model 602. The physics 3D model 606 canalso include physics modeling data (e.g., PHYSICS MODELING DATA shown inFIG. 6) generated by the machine learning process 604. The physicsmodeling data can be indicative of information associated with fluiddynamics, thermal dynamic and/or combustion dynamics. For instance, thephysics modeling data can be rendered on the physics 3D model 606 torepresent fluid flow, thermal characteristics, combustioncharacteristics and/or physics behavior for a device associated with thephysics 3D model 606. In one example, the physics modeling data cansimulate physical phenomena such as, but not limited to, compressiblefluid flow, incompressible fluid flow, buoyancy driven flow, rotatingcavity system flow, conduction heat transfer, convection heat transfer,radiation heat transfer, combustion equilibrium-chemistry, speciestransport, and/or other physics behavior.

Referring now to FIG. 7, there is illustrated an example system 700 thatfacilitates overlaying and/or integrating computer aided design drawingswith fluid models, according to an aspect of the subject disclosure.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

The system 700 can include point cloud data 702, a control volume 704,identification data 706 and computer aided design data 708. The pointcloud data 702 can be digital data indicative of information for a setof data values associated with a 3D coordinate system. The point clouddata 702 can define a shape, a geometry, a size, one or more surfacesand/or other physical characteristics of a device associated with a 3Dmodel. For instance, the point cloud data 702 can include 3D data points(e.g., 3D vertices) that form a shape, a structure and/or a set ofsurfaces of a device associated with a 3D model via a 3D coordinatesystem. The 3D coordinate system can be, for example, a 3D Cartesiancoordinate system that includes an x-axis, a y-axis and a z-axis. Assuch, a data value included in the point cloud data 702 can beassociated with an x-axis value, a y-axis value and a z-axis value toidentify a location of the data value with respect to the 3D coordinatesystem. In an embodiment, the point cloud data 702 can correspond to thepoint cloud data employed to generate the 3D model 602. The point clouddata 702 can be employed to generate the control volume 704 for a 3Dmodel (e.g., a control volume for the 3D model 602). The control volume704 can be a computational domain for a geometric feature of a 3D modelassociated with the point cloud data 702. For example, the controlvolume 704 can be an abstraction of a region of a device through which afluid (e.g., a liquid or a gas) and/or an electrical current flows. Inone example, the control volume 704 can correspond to a chamber of adevice. As such, geometric features of the control volume 704 can bedetermined using the point cloud data 702. The identification data 706can be indicative of an identifier for the control volume 704. In oneexample, the identification data 706 can include a tag that tags and/orlinks the control volume 704 to the computer aided design data 708. Thecomputer aided design data can be 3D computer aided design dataassociated with the device and/or the 3D model (e.g., the 3D model 602).In one example, the identification data 706 can include, for example,point-cloud geometric tags that identifies a geometry of the controlvolume 704, CAD curves parametric expressions that provide a set ofparameters for the control volume 704, and/or surfaces parametric tagsthat describe surface information for the control volume 704. As suchthe computer aided design data 708 can be bi-directionally linked to thecontrol volume 704 via the identification data 706 such that the controlvolume 704 is automatically updated when the computer aided design data708 is updated.

FIG. 8 illustrates an example 3D model 800, in accordance with variousaspects and implementations described herein. The 3D model 800 caninclude a device portion 802 of the 3D model 800 and point cloud data804 that forms a structure for the device portion 802 of the 3D model800. The point cloud data 804 can define a shape, a geometry, a size,one or more surfaces and/or other physical characteristics of the deviceportion 802 of the 3D model 800. For example, the point cloud data 804can include 3D data points (e.g., 3D vertices) that form a shape, astructure and/or a set of surfaces of the device portion 802 of the 3Dmodel 800. In an embodiment, the point cloud data 804 can correspond tothe point cloud data 702.

FIG. 9 illustrates an example 3D model 900, in accordance with variousaspects and implementations described herein. The 3D model 900 can, forexample, correspond to the physics 3D model 606 and/or a 3D modelgenerated by the fluid model tool component 102. The 3D model 900 canillustrate fluid dynamics, thermal dynamic and/or combustion dynamicswith respect to a design of a device. For example, the 3D model 900 canbe a 3D model where physics modeling data associated with fluiddynamics, thermal dynamic and/or combustion dynamics is rendered on adevice. In an aspect, the 3D model 900 can include a device portion 902of the 3D model 900 and physics modeling data 904 that is rendered onthe device portion 902. Visual characteristics (e.g., a color, a size, ahues, shading, etc.) of the physics modeling data 904 can be dynamicbased on a value of the physics modeling data 904. For instance, a firstportion of the physics modeling data 904 associated with first physicsmodeling information can comprise a first visual characteristics and asecond portion of the physics modeling data 904 associated with secondphysics modeling information can comprise a second visualcharacteristic. In an embodiment, the physics modeling data 904 can bedetermined by the machine learning component 106. In one example, thephysics modeling data 904 can be associated with a set of controlvolumes and/or a flow network related to fluid dynamics, thermal dynamicand/or combustion dynamics. In an embodiment, a 3D design environmentassociated with the 3D model 900 can include a heat bar 906. The heatbar 906 can include a set of colors that correspond to different valuesfor the physics modeling data 904. For example, a first color (e.g., acolor red) in the heat bar 906 can correspond to a first value for thephysics modeling data 904 and a second color (e.g., a color blue) in theheat bar 906 can correspond to a second value for the physics modelingdata 904. In another embodiment, a 3D design environment associated withthe 3D model 900 can include a side bar 908. The side bar 908 caninclude information to facilitate generation of the 3D model 900 and/orthe physics modeling data 904. For example, the side bar 908 canfacilitate selection of one or more sub-components (e.g., flow elements,tubes, orifices, bends valves, junctions, fans, compressors, anotherother component, etc.) that form the device portion 902 of the 3D model900. In another example, the side bar 908 can facilitate selection of atype of physics modeling data (e.g., flow dynamics, thermal dynamics,combustion dynamics, etc.) provided by the physics modeling data 904.

FIG. 10 illustrates an example 3D model 1000, in accordance with variousaspects and implementations described herein. The 3D model 1000 can, forexample, correspond to the physics 3D model 606 and/or a 3D modelgenerated by the fluid model tool component 102. The 3D model 1000 canillustrate fluid dynamics, thermal dynamic and/or combustion dynamicswith respect to a design of a device. For example, the 3D model 1000 canbe a 3D model where physics modeling data associated with fluiddynamics, thermal dynamic and/or combustion dynamics is rendered on adevice. In an aspect, the 3D model 1000 can include a device portion1002 of the 3D model 1000 and physics modeling data 1004 that isrendered on the device portion 1002. Visual characteristics (e.g., acolor, a size, a hues, shading, etc.) of the physics modeling data 1004can be dynamic based on a value of the physics modeling data 1004. Forinstance, a first portion of the physics modeling data 1004 associatedwith first physics modeling information can comprise a first visualcharacteristics and a second portion of the physics modeling data 1004associated with second physics modeling information can comprise asecond visual characteristic. In an embodiment, the physics modelingdata 1004 can be determined by the machine learning component 106. Inone example, the physics modeling data 1004 can be associated with a setof control volumes and/or a flow network related to fluid dynamics,thermal dynamic and/or combustion dynamics. In an embodiment, a 3Ddesign environment associated with the 3D model 1000 can include a heatbar 1006. The heat bar 1006 can include a set of colors that correspondto different values for the physics modeling data 1004. For example, afirst color (e.g., a color red) in the heat bar 1006 can correspond to afirst value for the physics modeling data 1004 and a second color (e.g.,a color blue) in the heat bar 1006 can correspond to a second value forthe physics modeling data 1004. In another embodiment, a 3D designenvironment associated with the 3D model 1000 can include a side bar1008. The side bar 1008 can include information to facilitate generationof the 3D model 1000 and/or the physics modeling data 1004. For example,the side bar 1008 can facilitate selection of one or more sub-components(e.g., flow elements, tubes, orifices, bends valves, junctions, fans,compressors, another other component, etc.) that form the device portion1002 of the 3D model 1000. In another example, the side bar 1008 canfacilitate selection of a type of physics modeling data (e.g., flowdynamics, thermal dynamics, combustion dynamics, etc.) provided by thephysics modeling data 1004.

FIG. 11 illustrates an example 3D model 1100, in accordance with variousaspects and implementations described herein. The 3D model 1100 can, forexample, correspond to the physics 3D model 606 and/or a 3D modelgenerated by the fluid model tool component 102. The 3D model 1100 canillustrate fluid dynamics, thermal dynamic and/or combustion dynamicswith respect to a design of a device. For example, the 3D model 1100 canbe a 3D model where physics modeling data associated with fluiddynamics, thermal dynamic and/or combustion dynamics is rendered on adevice. In an aspect, the 3D model 1100 can include a device portion1102 of the 3D model 1100 and physics modeling data 1104 that isrendered on the device portion 1102. Visual characteristics (e.g., acolor, a size, a hues, shading, etc.) of the physics modeling data 1104can be dynamic based on a value of the physics modeling data 1104. Forinstance, a first portion of the physics modeling data 1104 associatedwith first physics modeling information can comprise a first visualcharacteristics and a second portion of the physics modeling data 1104associated with second physics modeling information can comprise asecond visual characteristic. In an embodiment, the physics modelingdata 1104 can be determined by the machine learning component 106. Inone example, the physics modeling data 1104 can be associated with a setof control volumes and/or a flow network related to fluid dynamics,thermal dynamic and/or combustion dynamics. In an embodiment, a 3Ddesign environment associated with the 3D model 1100 can include a heatbar 1106. The heat bar 1106 can include a set of colors that correspondto different values for the physics modeling data 1104. For example, afirst color (e.g., a color red) in the heat bar 1106 can correspond to afirst value for the physics modeling data 1104 and a second color (e.g.,a color blue) in the heat bar 1106 can correspond to a second value forthe physics modeling data 1104. In another embodiment, a 3D designenvironment associated with the 3D model 1100 can include a side bar1108. The side bar 1108 can include information to facilitate generationof the 3D model 1100 and/or the physics modeling data 1104. For example,the side bar 1108 can facilitate selection of one or more sub-components(e.g., flow elements, tubes, orifices, bends valves, junctions, fans,compressors, another other component, etc.) that form the device portion1102 of the 3D model 1100. In another example, the side bar 1108 canfacilitate selection of a type of physics modeling data (e.g., flowdynamics, thermal dynamics, combustion dynamics, etc.) provided by thephysics modeling data 1104.

FIG. 12 illustrates an example 3D model 1200, in accordance with variousaspects and implementations described herein. The 3D model 1200 can, forexample, correspond to the physics 3D model 606 and/or a 3D modelgenerated by the fluid model tool component 102. The 3D model 1200 canillustrate fluid dynamics, thermal dynamic and/or combustion dynamicswith respect to a design of a device. For example, the 3D model 1200 canbe a 3D model where physics modeling data associated with fluiddynamics, thermal dynamic and/or combustion dynamics is rendered on adevice. In an aspect, the 3D model 1200 can include a device portion1202 of the 3D model 1200. The 3D model 1200 can also include firstphysics modeling data 1204 a, second physics modeling data 1204 b andthird physics modeling data 1204 c that are rendered on the deviceportion 1202. Visual characteristics (e.g., a color, a size, a hues,shading, etc.) of the first physics modeling data 1204 a, the secondphysics modeling data 1204 b and the third physics modeling data 1204 ccan be dynamic based on a value of the physics modeling data 1204. Forinstance, the first physics modeling data 1204 a can comprise a firstvisual characteristic (e.g., a yellow color) associated with a firstphysics modeling data value, the second physics modeling data 1204 b cancomprise a second visual characteristic (e.g., a green color) associatedwith a second physics modeling data value, and the third physicsmodeling data 1204 c can comprise a third visual characteristic (e.g., ablue color) associated with a third physics modeling data value. In anembodiment, the first physics modeling data 1204 a, the second physicsmodeling data 1204 b and the third physics modeling data 1204 c can bedetermined by the machine learning component 106. In one example, thefirst physics modeling data 1204 a, the second physics modeling data1204 b and the third physics modeling data 1204 c can be associated witha set of control volumes and/or a flow network related to fluiddynamics, thermal dynamic and/or combustion dynamics. In an embodiment,a 3D design environment associated with the 3D model 1200 can include aheat bar 1206. The heat bar 1206 can include a set of colors thatcorrespond to different values for the first physics modeling data 1204a, the second physics modeling data 1204 b and the third physicsmodeling data 1204 c. For example, a first color (e.g., a color yellow)in the heat bar 1206 can correspond to the first physics modeling datavalue associated with the first visual characteristic for the firstphysics modeling data 1204 a, a second color (e.g., a color green) inthe heat bar 1206 can correspond to the second physics modeling datavalue associated with the second visual characteristic for the secondphysics modeling data 1204 b, and a third color (e.g., a color blue) inthe heat bar 1206 can correspond to the third physics modeling datavalue associated with the third visual characteristic for the thirdphysics modeling data 1204 c. In another embodiment, a 3D designenvironment associated with the 3D model 1200 can include a side bar1208. The side bar 1208 can include information to facilitate generationof the 3D model 1200, the first physics modeling data 1204 a, the secondphysics modeling data 1204 b and/or the third physics modeling data 1204c. For example, the side bar 1208 can facilitate selection of one ormore sub-components (e.g., flow elements, tubes, orifices, bends valves,junctions, fans, compressors, another other component, etc.) that formthe device portion 1202 of the 3D model 1200. In another example, theside bar 1208 can facilitate selection of a type of physics modelingdata (e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.)provided by the first physics modeling data 1204 a, the second physicsmodeling data 1204 b and the third physics modeling data 1104 c.

The aforementioned systems and/or devices have been described withrespect to interaction between several components. It should beappreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentsmay be combined into a single component providing aggregatefunctionality. The components may also interact with one or more othercomponents not specifically described herein for the sake of brevity,but known by those of skill in the art.

FIGS. 13-16 illustrate methodologies and/or flow diagrams in accordancewith the disclosed subject matter. For simplicity of explanation, themethodologies are depicted and described as a series of acts. It is tobe understood and appreciated that the subject innovation is not limitedby the acts illustrated and/or by the order of acts, for example actscan occur in various orders and/or concurrently, and with other acts notpresented and described herein. Furthermore, not all illustrated actsmay be required to implement the methodologies in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the methodologies could alternatively berepresented as a series of interrelated states via a state diagram orevents. Additionally, it should be further appreciated that themethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such methodologies to computers. The termarticle of manufacture, as used herein, is intended to encompass acomputer program accessible from any computer-readable device or storagemedia.

Referring to FIG. 13, there illustrated is a methodology 1300 forproviding interdisciplinary fluid modeling using point cloud data,according to an aspect of the subject innovation. As an example, themethodology 1300 can be utilized in various applications, such as, butnot limited to, modeling systems, aviation systems, power systems,distributed power systems, energy management systems, thermal managementsystems, transportation systems, oil and gas systems, mechanicalsystems, machine systems, device systems, cloud-based systems, heatingsystems, HVAC systems, medical systems, automobile systems, aircraftsystems, water craft systems, water filtration systems, cooling systems,pump systems, engine systems, diagnostics systems, prognostics systems,machine design systems, medical device systems, medical imaging systems,medical modeling systems, simulation systems, enterprise systems,enterprise imaging solution systems, advanced diagnostic tool systems,image management platform systems, artificial intelligence systems,machine learning systems, neural network systems, etc. At 1302, a 3Dmodel of a mechanical device is generated (e.g., by modeling component104) based on point cloud data indicative of information for a set ofdata values associated with a 3D coordinate system. The point cloud datacan be digital data indicative of information for a set of data valuesassociated with a 3D coordinate system. The point cloud data can definea shape, a geometry, a size, one or more surfaces and/or other physicalcharacteristics of the device. For instance, the point cloud data caninclude 3D data points (e.g., 3D vertices) that form a shape, astructure and/or a set of surfaces of the device via a 3D coordinatesystem. The 3D coordinate system can be, for example, a 3D Cartesiancoordinate system that includes an x-axis, a y-axis and a z-axis. Assuch, a data value included in the point cloud data can be associatedwith an x-axis value, a y-axis value and a z-axis value to identify alocation of the data value with respect to the 3D coordinate. In anembodiment, the generating the 3D model can include integrating a first3D model associated with a first mechanical device and a second 3D modelassociated with a second mechanical device.

At 1304, fluid flow, thermal characteristics, combustion characteristicsand/or physics behavior associated with the 3D model are predicted(e.g., by machine learning component 106) based on input data and amachine learning process associated with the 3D model. For instance, themachine learning process can perform learning and/or can generateinferences with respect to fluid flow, thermal characteristics,combustion characteristics and/or physics behavior associated with the3D model. The input data can include fluid data, electrical data and/orchemical data associated with an input provided to a device associatedwith the 3D model. The physics behavior can be indicative of behaviorrelated to fluid dynamics, thermal dynamics and/or combustion dynamicsthroughout the device associated with the 3D model in response to theinput data. In an embodiment, the predicting can include performing afirst machine learning process associated with the first 3D model andperforming a second machine learning process associated with the second3D model.

At 1306, physics modeling data of the mechanical device is rendered(e.g., by 3D design component 108) based on the fluid flow, the thermalcharacteristics, the combustion characteristics and/or the physicsbehavior. For example, the physics modeling data can be indicative of avisual representation of the fluid flow, the thermal characteristics,the combustion characteristics and/or the physics behavior with respectto the 3D model. The physics modeling data can be rendered on the 3Dmodel as dynamic visual elements. In an embodiment, the rendering of thephysics modeling data can include providing a 3D design environmentassociated with the 3D model.

Referring to FIG. 14, there illustrated is a methodology 1400 forproviding interdisciplinary fluid modeling using point cloud data,according to another aspect of the subject innovation. As an example,the methodology 1400 can be utilized in various applications, such as,but not limited to, modeling systems, aviation systems, power systems,distributed power systems, energy management systems, thermal managementsystems, transportation systems, oil and gas systems, mechanicalsystems, machine systems, device systems, cloud-based systems, heatingsystems, HVAC systems, medical systems, automobile systems, aircraftsystems, water craft systems, water filtration systems, cooling systems,pump systems, engine systems, diagnostics systems, prognostics systems,machine design systems, medical device systems, medical imaging systems,medical modeling systems, simulation systems, enterprise systems,enterprise imaging solution systems, advanced diagnostic tool systems,image management platform systems, artificial intelligence systems,machine learning systems, neural network systems, etc. At 1402, a 3Dmodel of a mechanical device is generated (e.g., by modeling component104) based on point cloud data indicative of information for a set ofdata values associated with a 3D coordinate system. The point cloud datacan be digital data indicative of information for a set of data valuesassociated with a 3D coordinate system. The point cloud data can definea shape, a geometry, a size, one or more surfaces and/or other physicalcharacteristics of the device. For instance, the point cloud data caninclude 3D data points (e.g., 3D vertices) that form a shape, astructure and/or a set of surfaces of the device via a 3D coordinatesystem. The 3D coordinate system can be, for example, a 3D Cartesiancoordinate system that includes an x-axis, a y-axis and a z-axis. Assuch, a data value included in the point cloud data can be associatedwith an x-axis value, a y-axis value and a z-axis value to identify alocation of the data value with respect to the 3D coordinate. In anembodiment, the generating the 3D model can include integrating a first3D model associated with a first mechanical device and a second 3D modelassociated with a second mechanical device.

At 1404, a machine learning process associated with the 3D model isperformed (e.g., by machine learning component 106) to predict one ormore characteristics of the mechanical device. The machine learningprocess can perform learning and and/or can generate inferences topredict the one or more characteristics of the mechanical device. Theone or more characteristics can be related to fluid dynamics, thermaldynamics and/or combustion dynamics throughout the mechanical deviceassociated with the 3D model. For instance, the one or morecharacteristics can include fluid flow characteristics, thermalcharacteristics, combustion characteristics and/or physics behaviorcharacteristics.

At 1406, a 3D design environment associated with the 3D model thatrenders physics modeling data of the mechanical device is provided(e.g., by 3D design component 108) based on the machine learningprocess. The 3D design environment can apply one or more numericalschemes associated with the machine learning process to createpredictions for machine simulated conditions for the 3D model.Predictions associated with the machine learning process can bedisplayed and/or analyzed on a visual representation of the mechanicaldevice using post-processing associated with a graphical user interface.In an aspect, the 3D design environment can generate simulationpredictions for the one or more characteristics can be related to fluiddynamics, thermal dynamics and/or combustion dynamics throughout themechanical device associated with the 3D model. For instance, the 3Ddesign environment can generate simulation predictions for fluid flowcharacteristics, thermal characteristics, combustion characteristicsand/or physics behavior characteristics of the mechanical deviceassociated with the 3D model.

Referring to FIG. 15, there illustrated is a methodology 1500 forproviding interdisciplinary fluid modeling using point cloud data,according to an aspect of the subject innovation. As an example, themethodology 1500 can be utilized in various applications, such as, butnot limited to, modeling systems, aviation systems, power systems,distributed power systems, energy management systems, thermal managementsystems, transportation systems, oil and gas systems, mechanicalsystems, machine systems, device systems, cloud-based systems, heatingsystems, HVAC systems, medical systems, automobile systems, aircraftsystems, water craft systems, water filtration systems, cooling systems,pump systems, engine systems, diagnostics systems, prognostics systems,machine design systems, medical device systems, medical imaging systems,medical modeling systems, simulation systems, enterprise systems,enterprise imaging solution systems, advanced diagnostic tool systems,image management platform systems, artificial intelligence systems,machine learning systems, neural network systems, etc. At 1502, a 3Dmodel of a mechanical device is generated (e.g., by modeling component104) based on point cloud data indicative of information for a set ofdata values associated with a 3D coordinate system. The point cloud datacan be digital data indicative of information for a set of data valuesassociated with a 3D coordinate system. The point cloud data can definea shape, a geometry, a size, one or more surfaces and/or other physicalcharacteristics of the device. For instance, the point cloud data caninclude 3D data points (e.g., 3D vertices) that form a shape, astructure and/or a set of surfaces of the device via a 3D coordinatesystem. The 3D coordinate system can be, for example, a 3D Cartesiancoordinate system that includes an x-axis, a y-axis and a z-axis. Assuch, a data value included in the point cloud data can be associatedwith an x-axis value, a y-axis value and a z-axis value to identify alocation of the data value with respect to the 3D coordinate. In anembodiment, the generating the 3D model can include integrating a first3D model associated with a first mechanical device and a second 3D modelassociated with a second mechanical device.

At 1504, a machine learning process associated with the 3D model isperformed (e.g., by machine learning component 106) to predict one ormore characteristics of the mechanical device. The one or morecharacteristics can include, for example, fluid flow, thermalcharacteristics, combustion characteristics and/or physics behavior. Forinstance, the machine learning process can perform learning and/or cangenerate inferences with respect to fluid flow, thermal characteristics,combustion characteristics and/or physics behavior associated with the3D model. In an aspect, the machine learning process can also beperformed based on input data. The input data can include fluid data,electrical data and/or chemical data associated with an input providedto a device associated with the 3D model. The physics behavior can beindicative of behavior related to fluid dynamics, thermal dynamicsand/or combustion dynamics throughout the device associated with the 3Dmodel in response to the input data.

At 1506, physics modeling data of the mechanical device is generated(e.g., by machine learning component 106) based on the one or morecharacteristics of the mechanical device. The physics modeling data canbe indicative of a visual representation of the fluid flow, the thermalcharacteristics, the combustion characteristics and/or the physicsbehavior with respect to the 3D model.

At 1508, it is determined (e.g., by machine learning component 106)whether the machine learning process has generated new output. If yes,the methodology 1500 returns to 1506 to update the physics modeling databased on the new output. If no, the methodology 1500 proceeds to 1510.

At 1510, a graphical user interface that presents the 3D model via adisplay device and renders the physics modeling data on the 3D model isgenerated (e.g., by 3D design component 108). In an aspect, the physicsmodeling data can be rendered on the 3D model as dynamic visualelements. In an embodiment, generation of the graphical user interfacecan include providing a 3D design environment associated with the 3Dmodel.

At 1512, it is determined (e.g., by machine learning component 106)whether an input parameter for the physics modeling data has beenaltered. For example, it can be determined whether a new input parameterfor the machine learning process is provided via the graphical userinterface. If yes, the methodology 1500 returns to 1504 to perform a newmachine learning process based on the altered input parameters. If no,the methodology 1500 can end.

Referring to FIG. 16, there illustrated is a methodology 1600 forproviding interdisciplinary fluid modeling using point cloud data,according to an aspect of the subject innovation. As an example, themethodology 1600 can be utilized in various applications, such as, butnot limited to, modeling systems, aviation systems, power systems,distributed power systems, energy management systems, thermal managementsystems, transportation systems, oil and gas systems, mechanicalsystems, machine systems, device systems, cloud-based systems, heatingsystems, HVAC systems, medical systems, automobile systems, aircraftsystems, water craft systems, water filtration systems, cooling systems,pump systems, engine systems, diagnostics systems, prognostics systems,machine design systems, medical device systems, medical imaging systems,medical modeling systems, simulation systems, enterprise systems,enterprise imaging solution systems, advanced diagnostic tool systems,image management platform systems, artificial intelligence systems,machine learning systems, neural network systems, etc. At 1602, acomputational geometry for a control volume associated with a 3D modelof a device is determined (e.g., by modeling component 104 and/or pointcloud component 105) based on point cloud data indicative of informationfor a set of data values associated with a 3D coordinate system. Thecontrol volume can be a computational domain for a geometric feature ofthe 3D model associated with the point cloud data. For example, thecontrol volume can be an abstraction of a region of the device throughwhich a fluid (e.g., a liquid or a gas) and/or an electrical currentflows. In one example, the control volume can correspond to a chamber ofthe device. As such, geometric features of the control volume can bedetermined using the point cloud data. The point cloud data can bedigital data indicative of information for a set of data valuesassociated with a 3D coordinate system. The point cloud data can definea shape, a geometry, a size, one or more surfaces and/or other physicalcharacteristics of the device. For instance, the point cloud data caninclude 3D data points (e.g., 3D vertices) that form a shape, astructure and/or a set of surfaces of the device via a 3D coordinatesystem. The 3D coordinate system can be, for example, a 3D Cartesiancoordinate system that includes an x-axis, a y-axis and a z-axis. Assuch, a data value included in the point cloud data can be associatedwith an x-axis value, a y-axis value and a z-axis value to identify alocation of the data value with respect to the 3D coordinate.

At 1604, identification data indicative of an identifier for the controlvolume is generated (e.g., by modeling component 104 and/or point cloudcomponent 105). In one example, the identification data can include oneor more tags. For instance, the identification data can includepoint-cloud geometric tags that identifies a geometry of the controlvolume, CAD curves parametric expressions that provide a set ofparameters for the control volume, and/or surfaces parametric tags thatdescribe surface information for the control volume.

At 1606, the control volume is linked to CAD data for the device (e.g.,by modeling component 104 and/or point cloud component 105) based on theidentification data. In one example, the identification data can includethe tags to facilitate the link between the control volume and the CADdata. The CAD data can be 3D CAD data associated with the device and/orthe 3D model. As such the CAD data can be bi-directionally linked to thecontrol volume via the identification data such that the control volumeis automatically updated when the CAD data is updated.

At 1608, it is determined whether the CAD data has been modified.

For example, it can be determined whether a design of the deviceassociated with the CAD data has been modified. If no, the methodology1600 proceed to 1610. If yes, the methodology 1600 proceeds to 1612.

At 1610, the computational geometry for the control volume is maintained(e.g., by modeling component 104 and/or point cloud component 105). Forexample, the computational geometry for the control volume can remainedunchanged if the CAD data associated with the control volume isunchanged.

At 1612, the computational geometry for the control volume is updated(e.g., by modeling component 104 and/or point cloud component 105). Forexample, the computational geometry for the control volume can bemodified if the CAD data associated with the control volume is modified.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 17 and 18 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 17, a suitable environment 1700 for implementingvarious aspects of this disclosure includes a computer 1712. Thecomputer 1712 includes a processing unit 1714, a system memory 1716, anda system bus 1718. The system bus 1718 couples system componentsincluding, but not limited to, the system memory 1716 to the processingunit 1714. The processing unit 1714 can be any of various availableprocessors. Dual microprocessors and other multiprocessor architecturesalso can be employed as the processing unit 1714.

The system bus 1718 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1716 includes volatile memory 1720 and nonvolatilememory 1722. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1712, such as during start-up, is stored in nonvolatile memory 1722. Byway of illustration, and not limitation, nonvolatile memory 1722 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory 1720 includes random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such asstatic RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), doubledata rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM(SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM),and Rambus dynamic RAM.

Computer 1712 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 17 illustrates, forexample, a disk storage 1724. Disk storage 1724 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. The disk storage 1724 also can include storage media separatelyor in combination with other storage media including, but not limitedto, an optical disk drive such as a compact disk ROM device (CD-ROM), CDrecordable drive (CD-R Drive), CD rewritable drive (CD-RW Drive) or adigital versatile disk ROM drive (DVD-ROM). To facilitate connection ofthe disk storage devices 1724 to the system bus 1718, a removable ornon-removable interface is typically used, such as interface 1726.

FIG. 17 also depicts software that acts as an intermediary between usersand the basic computer resources described in the suitable operatingenvironment 1700. Such software includes, for example, an operatingsystem 1728. Operating system 1728, which can be stored on disk storage1724, acts to control and allocate resources of the computer system1712. System applications 1730 take advantage of the management ofresources by operating system 1728 through program modules 1732 andprogram data 1734, e.g., stored either in system memory 1716 or on diskstorage 1724. It is to be appreciated that this disclosure can beimplemented with various operating systems or combinations of operatingsystems.

A user enters commands or information into the computer 1712 throughinput device(s) 1736. Input devices 1736 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1714through the system bus 1718 via interface port(s) 1738. Interfaceport(s) 1738 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1740 usesome of the same type of ports as input device(s) 1736. Thus, forexample, a USB port may be used to provide input to computer 1712, andto output information from computer 1712 to an output device 1740.Output adapter 1742 is provided to illustrate that there are some outputdevices 1740 like monitors, speakers, and printers, among other outputdevices 1740, which require special adapters. The output adapters 1742include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1740and the system bus 1718. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1744.

Computer 1712 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1744. The remote computer(s) 1744 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1712. For purposes of brevity, only a memory storage device 1746 isillustrated with remote computer(s) 1744. Remote computer(s) 1744 islogically connected to computer 1712 through a network interface 1748and then physically connected via communication connection 1750. Networkinterface 1748 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1750 refers to the hardware/softwareemployed to connect the network interface 1748 to the bus 1718. Whilecommunication connection 1750 is shown for illustrative clarity insidecomputer 1712, it can also be external to computer 1712. Thehardware/software necessary for connection to the network interface 1748includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 18 is a schematic block diagram of a sample-computing environment1800 with which the subject matter of this disclosure can interact. Thesystem 1800 includes one or more client(s) 1810. The client(s) 1810 canbe hardware and/or software (e.g., threads, processes, computingdevices). The system 1800 also includes one or more server(s) 1830.Thus, system 1800 can correspond to a two-tier client server model or amulti-tier model (e.g., client, middle tier server, data server),amongst other models. The server(s) 1830 can also be hardware and/orsoftware (e.g., threads, processes, computing devices). The servers 1830can house threads to perform transformations by employing thisdisclosure, for example. One possible communication between a client1810 and a server 1830 may be in the form of a data packet transmittedbetween two or more computer processes.

The system 1800 includes a communication framework 1850 that can beemployed to facilitate communications between the client(s) 1810 and theserver(s) 1830. The client(s) 1810 are operatively connected to one ormore client data store(s) 1820 that can be employed to store informationlocal to the client(s) 1810. Similarly, the server(s) 1830 areoperatively connected to one or more server data store(s) 1840 that canbe employed to store information local to the servers 1830.

It is to be noted that aspects or features of this disclosure can beexploited in substantially any wireless telecommunication or radiotechnology, e.g., Wi-Fi; Bluetooth; Worldwide Interoperability forMicrowave Access (WiMAX); Enhanced General Packet Radio Service(Enhanced GPRS); Third Generation Partnership Project (3GPP) Long TermEvolution (LTE); Third Generation Partnership Project 2 (3GPP2) UltraMobile Broadband (UMB); 3GPP Universal Mobile Telecommunication System(UMTS); High Speed Packet Access (HSPA); High Speed Downlink PacketAccess (HSDPA); High Speed Uplink Packet Access (HSUPA); GSM (GlobalSystem for Mobile Communications) EDGE (Enhanced Data Rates for GSMEvolution) Radio Access Network (GERAN); UMTS Terrestrial Radio AccessNetwork (UTRAN); LTE Advanced (LTE-A); etc. Additionally, some or all ofthe aspects described herein can be exploited in legacytelecommunication technologies, e.g., GSM. In addition, mobile as wellnon-mobile networks (e.g., the Internet, data service network such asinternet protocol television (IPTV), etc.) can exploit aspects orfeatures described herein.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthis disclosure also can or may be implemented in combination with otherprogram modules. Generally, program modules include routines, programs,components, data structures, etc. that perform particular tasks and/orimplement particular abstract data types. Moreover, those skilled in theart will appreciate that the inventive methods may be practiced withother computer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., PDA, phone), microprocessor-based or programmable consumer orindustrial electronics, and the like. The illustrated aspects may alsobe practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. However, some, if not all aspects of thisdisclosure can be practiced on stand-alone computers. In a distributedcomputing environment, program modules may be located in both local andremote memory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component may be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers.

In another example, respective components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor. In such acase, the processor can be internal or external to the apparatus and canexecute at least a part of the software or firmware application. As yetanother example, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,wherein the electronic components can include a processor or other meansto execute software or firmware that confers at least in part thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

As used herein, the terms “example” and/or “exemplary” are utilized tomean serving as an example, instance, or illustration. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

Various aspects or features described herein can be implemented as amethod, apparatus, system, or article of manufacture using standardprogramming or engineering techniques. In addition, various aspects orfeatures disclosed in this disclosure can be realized through programmodules that implement at least one or more of the methods disclosedherein, the program modules being stored in a memory and executed by atleast a processor. Other combinations of hardware and software orhardware and firmware can enable or implement aspects described herein,including a disclosed method(s). The term “article of manufacture” asused herein can encompass a computer program accessible from anycomputer-readable device, carrier, or storage media. For example,computer readable storage media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical discs (e.g., compact disc (CD), digital versatile disc(DVD), blu-ray disc (BD) . . . ), smart cards, and flash memory devices(e.g., card, stick, key drive . . . ), or the like.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor may also beimplemented as a combination of computing processing units.

In this disclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory.

By way of illustration, and not limitation, nonvolatile memory caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), flashmemory, or nonvolatile random access memory (RAM) (e.g., ferroelectricRAM (FeRAM). Volatile memory can include RAM, which can act as externalcache memory, for example. By way of illustration and not limitation,RAM is available in many forms such as synchronous RAM (SRAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct RambusRAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM(RDRAM). Additionally, the disclosed memory components of systems ormethods herein are intended to include, without being limited toincluding, these and any other suitable types of memory.

It is to be appreciated and understood that components, as describedwith regard to a particular system or method, can include the same orsimilar functionality as respective components (e.g., respectively namedcomponents or similarly named components) as described with regard toother systems or methods disclosed herein.

What has been described above includes examples of systems and methodsthat provide advantages of this disclosure. It is, of course, notpossible to describe every conceivable combination of components ormethods for purposes of describing this disclosure, but one of ordinaryskill in the art may recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes computerexecutable components stored in the memory, wherein the computerexecutable components comprise: a modeling component that determines afirst control volume associated with a first three-dimensional model ofa first device and a second control volume associated with a secondthree-dimensional model of a second device, wherein the modelingcomponent determines the first control volume and the second controlvolume based on respective point cloud data indicative of respectiveinformation for respective sets of data values associated with athree-dimensional coordinate system, wherein the modeling componentupdates the first control volume, the second control volume, or both thefirst control volume and the second control volume in response tomodifications of respective computer aided design data associated withthe first device and the second device; and a three-dimensional designcomponent that provides a three-dimensional design environmentassociated with a machine that comprises the first device and the seconddevice, wherein the three-dimensional design environment rendersmodeling data of the machine based on a machine learning processassociated with the first three-dimensional model indicative of a firstflow through the first device and the second three-dimensional modelindicative of a second flow through the second device.
 2. The system ofclaim 1, wherein the modeling component generates first identificationdata indicative of a first identifier for the first control volume, andsecond identification data indicative of a second identifier for thesecond control volume.
 3. The system of claim 1, wherein the modelingcomponent determines a first computational geometry for the firstcontrol volume associated with the first three-dimensional model andsecond computational geometry for the second control volume associatedwith the second three-dimensional model based on point cloud data. 4.The system of claim 1, wherein the computer executable componentsfurther comprise: a machine learning component that predicts a first setof characteristics of the first device based on the first flow and asecond set of characteristics of the second device based on the secondflow.
 5. The system of claim 4, wherein the machine learning componentperforms the machine learning process based on the first control volumeassociated with the first three-dimensional model and the second controlvolume associated with the second three-dimensional model.
 6. The systemof claim 1, wherein the modeling component determines first geometricdata indicative of first geometric features of the first control volumeand first surface data indicative of first surface information for thefirst control volume and second geometric data indicative of secondgeometric features of the second control volume and second surface dataindicative of second surface information for the second control volume.7. A method, comprising: determining, by a system comprising aprocessor, a first flow network, represented as a first control volumeassociated with a first three-dimensional model of a first device and asecond flow network, represented as a second control volume, associatedwith a second three-dimensional model of a second device; updating, bythe system, the first control volume in response to a first modificationof first computer aided design data associated with the first device anda second modification of the second control volume in response tomodification of second computer aided design data associated with thesecond device; determining, by the system, characteristics of a machinethat comprise the first device and the second device, wherein thedetermining is based on respective machine learning processes associatedwith the first three-dimensional model and the second three-dimensionalmodel; and rendering, by the system, a three-dimensional designenvironment of the machine based on the characteristics, the firstthree-dimensional model of the first device, and the secondthree-dimensional model of the second device.
 8. The method of claim 7,further comprising: determining, by the system, a first computationalgeometry for the first device based on first point cloud data determinedfor the first device, and a second computational geometry for the seconddevice based on second point cloud data for the second device, whereinthe first point cloud data and the second point cloud data aredetermined based on a three-dimensional coordinate system.
 9. The methodof claim 7, further comprising: determining, by the system, a firstcomputational geometry for the first control volume associated with thefirst three-dimensional model and second computational geometry for thesecond control volume associated with the second three-dimensional modelbased on point cloud data.
 10. The method of claim 7, furthercomprising: generating, by the system, first identification dataindicative of a first identifier for the first control volume and secondidentification data indicative of a second identifier for the secondcontrol volume.
 11. The method of claim 10, further comprising: inresponse to the first modification and the second modification,updating, by the system, the first control volume based on the firstidentification data and the second control volume based on the secondidentification data.
 12. A non-transitory computer readable mediacomprising instructions that, in response to execution, cause a systemcomprising a processor to perform operations, comprising: determining afirst flow network of a first device and a second flow network of asecond device, wherein the first flow network is represented as a firstcontrol volume of a first three-dimensional model of the first deviceand the second flow network is represented as a second control volume ofa second three-dimensional model of the second device; updating thefirst control volume, the second control volume, or both the firstcontrol volume and the second control volume, based on a modification ofcomputer aided design data associated with the first device, the seconddevice, or both the first device and the second device; determiningcharacteristics of a machine based on performing a first machinelearning process associated with the first three-dimensional model and asecond machine learning process associated with the secondthree-dimensional model; and outputting, on a display of a user device,a three-dimensional design environment associated with the machine basedon the characteristics of the machine, wherein the machine comprises thefirst device and the second device, and wherein the three-dimensionaldesign environment comprises the first three-dimensional model and thesecond three-dimensional model.
 13. The non-transitory computer readablemedia of claim 12, wherein the operations further comprise: determininga first computational geometry for the first control volume associatedwith the first three-dimensional model based on first point cloud data;and determining a second computational geometry for the second controlvolume associated with the second three-dimensional model based onsecond point cloud data, wherein the first point cloud data and thesecond point cloud data are indicative of information for respectivesets of data values associated with the first device and the seconddevice with respect to a three-dimensional coordinate system.
 14. Thenon-transitory computer readable media of claim 12, wherein theoperations further comprise: generating first identification dataindicative of a first identifier for the first control volume and secondidentification data indicative of a second identifier for the secondcontrol volume.
 15. The non-transitory computer readable media of claim14, wherein the operations further comprise: updating, based on thefirst identification data, the first control volume, in response to themodification of the computer aided design data associated with the firstdevice; and updating, based on the second identification data, thesecond control volume, in response to the modification of the computeraided design data associated with the second device.
 16. The method ofclaim 7, wherein the first flow network and the second flow networkrepresent respective flows of fluid through the first device and thesecond device.
 17. The method of claim 7, wherein the first device andthe second device are electrical devices, and wherein the first flownetwork and the second flow network represent respective flows ofelectricity through the first device and the second device.
 18. Themethod of claim 7, wherein the first flow network and the second flownetwork represent respective flows of heat transfer through the firstdevice and the second device, and wherein the three-dimensional designenvironment is a heat transfer design environment.
 19. The method ofclaim 7, wherein the three-dimensional design environment is a flowintegrated design environment.
 20. The method of claim 7, wherein thethree-dimensional design environment is a combustion design environment.